Man-machine cooperation interaction method for rapidly identifying bearing fault

Abstract

The invention discloses a man-machine cooperation interaction method for rapidly identifying bearing faults. The method comprises the following steps: calculating a bearing characteristic frequency through a basic characteristic frequency, a rotation speed ratio and a rotation speed; calculating a waveform spectrum and an envelope spectrum by using FFT and Hilbert transform; searching a peak value in the frequency domain data by adopting a peak value searching algorithm and an amplitude limiting method; peak value and characteristic frequency matching: based on acceptable deviation matching peak value frequency, characteristic frequency and frequency multiplication thereof, through automatic calculation and matching of bearing characteristic frequency, fault diagnosis time is substantially shortened, and work efficiency of engineers is improved; an engineer does not need to carry out complex frequency calculation, and fault information can be quickly obtained only through simple operation; the system automatically carries out frequency matching calculation, errors caused by manual operation are reduced, and the diagnosis accuracy is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of bearing fault detection, and in particular relates to a human-machine collaborative interaction method for quickly identifying bearing faults. Background Art

[0002] At present, bearing fault diagnosis mainly relies on the calculation method of characteristic frequency. Although these methods can provide basic fault information, they have the following problems in practical application: Devices often contain multiple characteristic frequencies, and the process of finding these frequencies is tedious and time consuming.

[0003] After discovering abnormal frequencies in the spectrum graph, engineers still need to perform complex calculations based on the relevant rotational speeds, resulting in low fault identification efficiency and difficulty in quickly locating the problem. Summary of the invention

[0004] The purpose of the present invention is to provide a human-machine collaborative interaction method for quickly identifying bearing faults to solve the problem.

[0005] To achieve the above object, the present invention provides the following technical solution: a human-machine collaborative interaction method for quickly identifying bearing faults, the specific steps are as follows: Step 1: Input existing information and select the corresponding model of standard parts directly from the bearing library to obtain the characteristic frequencies of the inner ring, outer ring, rolling element and cage; you can also input the characteristic frequencies of the equipment in other ways And its corresponding speed ratio ; Step 2: Calculate the bearing characteristic frequency and the basic characteristic frequency The basic characteristic frequency is the characteristic frequency of the inner ring, outer ring, rolling element and cage obtained in step 1. , speed , calculate the bearing characteristic frequency:

[0006] Traverse all bearings of the equipment and calculate the corresponding bearing characteristic frequencies ; Traversing the equipment specifically calculates the characteristic frequencies of all bearings included in the monitored equipment for screening; Step 3: Calculate the characteristic spectrum and perform FFT conversion on the waveform obtained by the corresponding device to obtain the waveform spectrum ; Perform Hilbert transform on the waveform acquired by the corresponding device to obtain the waveform envelope spectrum ; The above two transformations are performed on the same waveform in no particular order to obtain a spectrum for comparison; Step 4: Search for interval peaks in the frequency domain data, search for peaks in the specified frequency domain interval, waveform spectrum corresponding to (medium and low frequency 1HZ-1000HZ), waveform envelope spectrum (corresponding to low frequency interval 1HZ-500HZ); Step 5: Peak and characteristic frequency matching, based on spectrum parameters (spectral resolution , spectrum range ), calculate the acceptable deviation ; Acceptable deviation ; Matches peak frequency and characteristic frequency and its 1 to 5 multiples based on acceptable deviation , is the characteristic frequency number, is the frequency multiple number; when the difference satisfy , then it is the matching characteristic frequency . Returns the matching characteristic frequency, that is, the true characteristic frequency .

[0007] Preferably, the peak value search method in step 4 adopts a peak value search algorithm (both left and right are less than the current value); the peak value screening method adopts amplitude limitation, and the peak amplitude ,when When is the effective peak value; the peak amplitude threshold , the multiple parameters (mean , effective value , maximum value ) is calculated by weighted calculation. Returns the corresponding peak frequency .

[0008] The technical effects and advantages of the present invention are as follows: by automatically calculating and matching the characteristic frequency of the bearing, the time for fault diagnosis is significantly shortened, and the work efficiency of engineers is improved. Engineers do not need to perform complex frequency calculations, but can quickly obtain fault information through simple operations. The system automatically performs frequency matching calculations, which reduces errors caused by human operations and improves the accuracy of diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a schematic diagram of the operation steps of the present invention; Figure 2 It is the peak spectrum in the embodiment of the present invention. DETAILED DESCRIPTION

[0010] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0011] The present invention provides a human-machine collaborative interaction method for quickly identifying bearing faults as shown in the figure, and the bearing information is input: Enter the relevant information of the bearing into the system. If it is a standard part, you can select the corresponding model from the bearing library to obtain the characteristic frequencies of the inner ring, outer ring, rolling element and cage. At the same time, the system also supports inputting the characteristic frequencies of the equipment through other methods. And its corresponding speed ratio .

[0012] Calculate the bearing eigenfrequency: According to the basic characteristic frequency (Based on 1rpm), the basic characteristic frequency is the characteristic frequency of the inner ring, outer ring, rolling element and cage obtained above, and the speed ratio (Ratio to the power input shaft speed), speed (power input shaft speed), calculate the bearing characteristic frequency: ; Traverse all bearings of the equipment and calculate the corresponding bearing characteristic frequencies .

[0013] Compute the characteristic spectrum: Perform FFT conversion on the waveform obtained by the corresponding device to obtain the waveform spectrum .

[0014] Perform Hilbert transform on the waveform acquired by the corresponding device to obtain the waveform envelope spectrum .

[0015] Find the interval peak value for frequency domain data: Find the peak value for the specified frequency domain interval, waveform spectrum Corresponding (mid-low frequency 1HZ-1000HZ), waveform envelope spectrum (corresponding to the low frequency range of 1HZ-500HZ). The peak search method uses the peak search algorithm (both left and right are less than the current value). The peak screening method uses amplitude limitation, peak amplitude ,when The peak value is the effective peak value. , the multiple parameters (mean , effective value , maximum value ) is calculated by weighted calculation. Returns the corresponding peak frequency .

[0016] The peak matches the characteristic frequency: Based on spectral parameters (spectral resolution , spectrum range ), calculate the acceptable deviation .

[0017] Acceptable deviation ; Matches peak frequency and characteristic frequency and its 1 to 5 multiples based on acceptable deviation , is the characteristic frequency number, is the frequency multiple number. satisfy , then it is the matching characteristic frequency . Returns the matching characteristic frequency, that is, the true characteristic frequency .

[0018] Example: Bearing information entry: Enter the relevant information of the bearing into the system. If it is a standard part, you can select the corresponding model from the bearing library to obtain the characteristic frequencies of the inner ring, outer ring, rolling element and cage. At the same time, the system also supports inputting the characteristic frequencies of the equipment through other methods ( ) and its corresponding speed ratio ( ).

[0019] Calculate the bearing eigenfrequency: According to the basic characteristic frequency (Based on 1rpm), speed ratio (Ratio to the power input shaft speed), speed (power input shaft speed), calculate the bearing characteristic frequency: ; Traverse all bearings of the equipment and calculate the corresponding bearing characteristic frequencies .

[0020] Compute the characteristic spectrum: Perform FFT conversion on the waveform obtained by the corresponding device to obtain the waveform spectrum .

[0021] Perform Hilbert transform on the waveform acquired by the corresponding device to obtain the waveform envelope spectrum .

[0022] Perform interval peak search for frequency domain data; Find the peak value for the specified frequency domain interval, waveform spectrum Corresponding (mid-low frequency 1HZ-1000HZ), waveform envelope spectrum (corresponding to the low frequency range of 1HZ-500HZ). The peak search method uses the peak search algorithm (both left and right are less than the current value). The peak screening method uses amplitude limitation, peak amplitude ,when The peak value is the effective peak value. , the multiple parameters (mean , effective value , maximum value ) is calculated by weighted calculation. Returns the corresponding peak frequency .

[0023] The peak matches the characteristic frequency: Based on spectral parameters (spectral resolution , spectrum range ), calculate the acceptable deviation .

[0024] Acceptable deviation ; Matches peak frequency and characteristic frequency and its 1 to 5 multiples based on acceptable deviation , is the characteristic frequency number, is the frequency multiple number. satisfy , then it is the matching characteristic frequency . Returns the matching characteristic frequency, that is, the true characteristic frequency .

[0025] Calculate the eigenfrequencies: Inner race characteristic frequency =5 Hz; Outer ring characteristic frequency =10 Hz; Rolling element characteristic frequency =3 Hz; Cage characteristic frequency =4 Hz; Input shaft =3; Bearing Correspondence =1; In this case, these characteristic frequencies are calculated for the same component and the speed ratio is Both are 1 / 3; ; Hz; Find the peak frequency: Applying the peak search algorithm to the spectrum in the figure yields a spectrum with peak values ​​of {8.00, 10.00, 50.00}; Calculate the acceptable deviation: ; .

[0026] Match characteristic frequencies and peaks and mark fault types: The spectrum matched based on the spectrum is {8.00, 10.00, 50.00} Hz, and the corresponding amplitude is {0.28, 0.18, 0.25}; Labeled according to frequency type as ['Inner ring @ 1X', 'Rolling @ 2X', 'Outer ring @ 3X'].

[0027] Additional information: Bearing model: default f1=5, f2=10, f3=3, f4=4; R0=3, Rn=1, the ratio is 3:1; The characteristic frequencies corresponding to the speed of 300.0 rpm are [8.333333333333332, 16.6666666666666664, 5.0, 6.6666666666666666] Hz; FFT Peaks: Frequency:8.00 Hz, Magnitude: 0.28; Frequency:10.00 Hz, Magnitude: 0.18; Frequency: 50.00 Hz, Magnitude: 0.25; index_characteristic_freq_list [1, 7, 10]; Matched characteristic frequencies: [8.333333333333332, 10.0, 49.999999999999999] @ ['inner circle@1X', 'rolling@2X', 'outer circle@3X'].

[0028] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A human-machine collaborative interaction method for quickly identifying bearing faults, characterized in that: The specific steps are as follows: Step 1: Input existing information and select the corresponding model of standard parts directly from the bearing library to obtain the characteristic frequencies of the inner ring, outer ring, rolling element and cage And its corresponding speed ratio ; Step 2: Calculate the bearing characteristic frequency and the basic characteristic frequency The basic characteristic frequency is the characteristic frequency of the inner ring, outer ring, rolling element and cage obtained in step 1; the speed ratio , speed , calculate the bearing characteristic frequency: ; Traverse all bearings of the equipment and calculate the corresponding bearing characteristic frequencies ; Step 3: Calculate the characteristic spectrum: Perform FFT conversion on the waveform obtained by the corresponding device to obtain the waveform spectrum ; Perform Hilbert transform on the waveform acquired by the corresponding device to obtain the waveform envelope spectrum ; Step 4: Search for the peak value of the frequency domain data and the peak value of the specified frequency domain interval; Step 5: Peak and characteristic frequency matching based on spectrum resolution , spectrum range Spectral parameters, calculation of acceptable deviation ; Acceptable deviation ; Match peak frequency and characteristic frequency and peak frequency and characteristic frequency 1 to 5 times based on acceptable deviation , is the characteristic frequency number, is the frequency multiple number; when the difference satisfy , then it is the matching characteristic frequency ; Return the matching characteristic frequency, that is, the true characteristic frequency .

2. A human-machine collaborative interaction method for quickly identifying bearing faults according to claim 1, characterized in that: The traversal device in step 2 specifically calculates the characteristic frequencies of all bearings included in the monitored equipment for screening.

3. The human-machine collaborative interaction method for quickly identifying bearing faults according to claim 1 is characterized in that: The waveform obtained by the corresponding device in step 3 is subjected to FFT conversion to obtain the waveform spectrum ; Perform Hilbert transform on the waveform acquired by the corresponding device to obtain the waveform envelope spectrum ; The two transformations are performed on the same waveform in no particular order to obtain a graph for comparison.

4. The human-machine collaborative interaction method for quickly identifying bearing faults according to claim 1 is characterized in that: The peak value search method in step 4 adopts a peak value search algorithm; the peak value screening method adopts amplitude limitation, and the peak amplitude ,when When is the effective peak value; the peak amplitude threshold , calculated by weighted calculation of multiple parameters corresponding to the interval spectrum amplitude; returns the corresponding peak frequency .

5. The human-machine collaborative interaction method for quickly identifying bearing faults according to claim 1 is characterized in that: The waveform spectrum in step 4 corresponds to the mid-low frequency 1HZ-1000HZ, and the waveform envelope spectrum corresponds to the low frequency range 1HZ-500HZ.

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